package org.example
import org.apache.spark.{SparkConf, SparkContext}

object yjs {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("SalaryFilter").setMaster("local")
    val sc = new SparkContext(conf)
    val first_half = sc.textFile("C:\\Users\\Administrator\\Desktop\\Employee_salary_first_half.csv")
    val second_half = sc.textFile("C:\\Users\\Administrator\\Desktop\\Employee_salary_second_half.csv")
    val drop_first = first_half.mapPartitionsWithIndex((ix,it) => {
      if (ix ==0) it.drop(1)
      else
        it
    })
    val drop_second = second_half.mapPartitionsWithIndex((ix, it) => {
      if (ix == 0) it.drop(1)
      else
        it
    })
    val split_first = drop_first.map(
      Line => {val data = Line.split(",");(data(1),data(6).toInt)})
    val split_second = drop_second.map(
      Line => {val data = Line.split(",");(data(1),data(6).toInt)})
    val filter_first=split_first.filter(x => x._2 > 200000).map(x => x._1)
    val filter_second=split_second.filter(x => x._2 > 200000).map(x => x._1)
    val name=filter_first.union(filter_second).distinct()
    name.collect().foreach(println)

    val salary = split_first.union(split_second)
    val avg_salary = salary.combineByKey(
       count => (count, 0),
      (acc:(Int, Int), count) => (acc._1 + count, acc._2 + 0),
      (acc1:(Int, Int), acc2:(Int, Int)) => (acc1._1 + acc2._1, acc1._2 + acc2._2)
    )
    avg_salary.map(x => (x._1, x._2._1.toDouble / 12)).foreach(println)
    val total = split_first.join(split_second).join(salary).join(avg_salary).map(
      x => Array(x._1, x._2._1._1._1, x._2._1._1._2,
        x._2._1._2, x._2._2).mkString(
        ","))
    total.repartition(1).saveAsTextFile("C:\\Users\\Administrator\\Desktop\\save")

    sc.stop()

  }
}
